The 21st International Conference on Discovery Science (DS 2018) provides
an open forum for intensive discussions and exchange of new ideas among
researchers working in the area of Discovery Science.

The scope of the conference includes the development and analysis of
methods for discovering scientific knowledge, coming from machine
learning, data mining, intelligent data analysis, big data analysis as well as
their application in various scientific domains.

We welcome papers that focus on the analysis of different types of massive
and complex data, including structured, spatio-temporal and network data.
We particularly welcome papers addressing applications. Finally, we would
like to encourage contributions from the areas of computational
scientific discovery, mining scientific data, computational creativity and
discovery informatics.

DS 2018 will be co-located with ISMIS 2018, the 24th International
Symposium on Methodologies for Intelligent Systems. The two conferences
will be held in parallel, and will share their invited talks.

TOPICS

We invite submissions of research papers addressing all aspects of
discovery science. We particularly welcome contributions that discuss the
application of data analysis, data mining and other support techniques for
scientific discovery including, but not limited to, biomedical, astronomical
and other physics domains. Applications to massive, heterogeneous,
continuous or imprecise data sets are of particular interests. Possible topics
include, but are not limited to:

Papers may contain up to fifteen (15) pages and must be formatted
according to the layout supplied by Springer-Verlag for the Lecture Notes
in Computer Science series. Submitted papers may not have appeared in or
be under consideration for another workshop, conference or a journal,
nor may they be under review or submitted to another forum during the DS
2018 review process

The DS 2018 proceedings will be published by Springer in LNAI
(Lecture Notes in Artificial Intelligence) and will be available at the
conference.

Authors of best papers will be invited to submit their extended versions to
the Machine Learning journal (https://link.springer.com/journal/10994)
published by Springer. Fast Track Processing will be used to have them
reviewed and published.